Maximum likelihood estimation is a popular method for estimating parameters in a statistical model. As its name suggests, maximum likelihood estimation involves finding the value of the parameter that maximizes the likelihood function (or, equivalently, maximizes the log-likelihood function). This value is called the maximum likelihood estimate, or MLE. It seems natural to ask about the accuracy of an MLE: how far from the "true" value of the parameter can we expect the MLE to be? This vignette answers this question in a simple but important case: maximum likelihood estimation based on independent and identically distributed (i.i.d.) data from a model.

In the Model Fitting and Recommendation Systems section, you will learn how i applying the machine learning algorithms.

In the Classification with More than Two Classes and the Caret Package section, you will learn how to overcome the curse of dimensionality using methods that adapt to higher dimensions and how to use the caret package to implement many different machine learning algorithms.

This report is part of the final project capstone to obtain the ‘Professional Certificate in Master of Data Science’ emited by Harvard University (HarvadX), platform for education and learning. The main objective is to create a recommendation system using the Heart Disease UCI dataset, and it must be done training a machine learning algorithm using the inputs in one subset to predict in the validation set.

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